MCPeSe: Monte Carlo penalty selection for graphical lasso | Zendy

Markku Kuismin | Zendy; Mikko J. Sillanpää | Zendy

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MCPeSe: Monte Carlo penalty selection for graphical lasso

Author(s) -

Markku Kuismin,

Mikko J. Sillanpää

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa734

Subject(s) - computer science , frequentist inference , scalability , lasso (programming language) , markov chain monte carlo , regularization (linguistics) , source code , model selection , monte carlo method , bayesian probability , mathematical optimization , algorithm , machine learning , bayesian inference , artificial intelligence , mathematics , statistics , database , world wide web , operating system

Graphical lasso (Glasso) is a widely used tool for identifying gene regulatory networks in systems biology. However, its computational efficiency depends on the choice of regularization parameter (tuning parameter), and selecting this parameter can be highly time consuming. Although fully Bayesian implementations of Glasso alleviate this problem somewhat by specifying a priori distribution for the parameter, these approaches lack the scalability of their frequentist counterparts.

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